Detection of Fileless Malware through Network Traffic Analysis
Date
Authors
Ajmal, Ayesha
Doborjeh, Maryam
Gutierrez, Jairo
Supervisor
Item type
Conference Contribution
Degree name
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Abstract
The rapid growth of fileless malware raises a fundamental challenge to existing cybersecurity frameworks. These malwares operate entirely within a system’s volatile memory without creating malicious files on the disk. This research aims to overcome a critical gap in Network Intrusion Detection System (NIDS) by proposing a novel hybrid deep-learning framework. Traditional signature-based detection methods prove ineffective against these memory-resident threats, consequently this investigation details advanced feature extraction methodologies which can identify fileless malware using Network Packet Capture (PCAP) files. This study will employ Design Science Research (DSR) integrating it with a Design-Oriented Machine Learning (DS-ML) methodology which ensures systematic and rigorous development and evaluation process. Key contributions of this research will be: 1) holistic development of feature extraction mechanism that effectively captures fileless malware behavior within network traffic, 2) proposing a hybrid deep-learning model for optimizing the detection techniques for fileless malware, and 3) constituting specific evaluation metrics to measure the accuracy of detecting fileless malware. The resultant framework will discuss the limitations that are present in the existing approaches that primarily focus on detecting file-based malware.Description
Keywords
4605 Data Management and Data Science, 4606 Distributed Computing and Systems Software, 46 Information and Computing Sciences, 4604 Cybersecurity and Privacy, Machine Learning and Artificial Intelligence, Networking and Information Technology R&D (NITRD)
Source
2025 IEEE 35th International Telecommunication Networks and Applications Conference (ITNAC). 26-28 November 2025. Christchurch, New Zealand. ISBN: 979-8-3315-7918-0
Publisher's version
Rights statement
This is the Author's Accepted Manuscript of a conference paper presented at the 2025 IEEE 35th International Telecommunication Networks and Applications Conference (ITNAC). The Version of Record is available at DOI: 10.1109/itnac66378.2025.11302628